Comparison between the ages for the types (after controlling for gender)¶

Questions

What are the differences between the ages for the different types
Do we observe the same changes as globally?

Age effect - General Questions

What are the differences between the ages?
Which genes and pathways are differentially expressed between 8w and 52w, between 52w and 104w, between 8w and 104w? Are they the same? Is there a gradient?
Are they different for the two genders?
Are they different for the two types?

Loads¶

Libraries and functions¶

source("load_libraries.R")

Warning message in is.na(x[[i]]):
“is.na() applied to non-(list or vector) of type 'environment'”Warning message in rsqlite_fetch(res@ptr, n = n):
“Don't need to call dbFetch() for statements, only for queries”

==========================================================================
*
*  Package WGCNA 1.63 loaded.
*
*    Important note: It appears that your system supports multi-threading,
*    but it is not enabled within WGCNA in R. 
*    To allow multi-threading within WGCNA with all available cores, use 
*
*          allowWGCNAThreads()
*
*    within R. Use disableWGCNAThreads() to disable threading if necessary.
*    Alternatively, set the following environment variable on your system:
*
*          ALLOW_WGCNA_THREADS=<number_of_processors>
*
*    for example 
*
*          ALLOW_WGCNA_THREADS=4
*
*    To set the environment variable in linux bash shell, type 
*
*           export ALLOW_WGCNA_THREADS=4
*
*     before running R. Other operating systems or shells will
*     have a similar command to achieve the same aim.
*
==========================================================================


Allowing multi-threading with up to 4 threads.
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."

source("functions.R")

Data¶

load("../results/dge/gene_length.RData")
load("../results/dge/metadata.RData")
load("../results/dge/norm_counts.RData")
load("../results/dge/dge.RData")

load("../results/dge/dge_net.RData")
load("../results/dge/dge_layout.RData")
load("../results/dge/dge_net_connected_gene_colors.RData")
load("../results/dge/dge_net_pal2.RData")

module_nb = length(unique(connected_gene_colors))
#pal2 = c(pal2, "white", "black")

# Interactions between ages and types (after controlling gender)
SPF_52w_8w = results(dge,contrast= c(0,0,0,0,1,0,1/2,0,0,0), alpha=0.05, test="Wald")
GF_52w_8w = results(dge,contrast= c(0,0,0,0,1,0,1/2,0,0,1), alpha=0.05, test="Wald")
SPF_104w_8w = results(dge,contrast= c(0,0,0,1,0,1/2,0,0,0,0), alpha=0.05, test="Wald")
GF_104w_8w = results(dge,contrast= c(0,0,0,1,0,1/2,0,0,1,0), alpha=0.05, test="Wald")
SPF_104w_52w = results(dge,contrast= c(0,0,0,1,-1,1/2,-1/2,0,0,0), alpha=0.05, test="Wald")
GF_104w_52w = results(dge,contrast= c(0,0,0,1,-1,1/2,-1/2,0,1,-1), alpha=0.05, test="Wald")

SPF_samples = c(
    'SPF_8w_F_1_2','SPF_8w_F_3_2','SPF_8w_F_4_2','SPF_8w_F_5_2','SPF_8w_M_1_2','SPF_8w_M_2_2','SPF_8w_M_3_2','SPF_8w_M_4_2',
    'SPF_52w_F_1_2','SPF_52w_F_2_2','SPF_52w_F_3_2','SPF_52w_F_4_2','SPF_52w_F_5_2','SPF_52w_F_6_2','SPF_52w_M_1_2','SPF_52w_M_2_2','SPF_52w_M_3_2','SPF_52w_M_4_2','SPF_52w_M_5_2',
    'SPF_104w_F_1_2','SPF_104w_F_2_2','SPF_104w_F_3_2','SPF_104w_M_1_2','SPF_104w_M_2_2','SPF_104w_M_3_2','SPF_104w_M_4_2','SPF_104w_M_5_2','SPF_104w_M_6_2','SPF_104w_M_7_2','SPF_104w_M_8_2','SPF_104w_M_9_2','SPF_104w_M_10_2','SPF_104w_M_11_2','SPF_104w_M_12_2','SPF_104w_M_13_2','SPF_104w_M_14_2')
GF_samples = c(
    'GF_8w_F_1_2','GF_8w_F_2_2','GF_8w_F_3_2','GF_8w_F_4_2','GF_8w_F_5_2','GF_8w_M_1_2','GF_8w_M_2_2','GF_8w_M_3_2','GF_8w_M_4_2',
    'GF_52w_F_1_2','GF_52w_F_2_2','GF_52w_F_3_2','GF_52w_F_4_2','GF_52w_F_5_2','GF_52w_F_6_2','GF_52w_M_1_2','GF_52w_M_2_2','GF_52w_M_3_2','GF_52w_M_4_2',
    'GF_104w_F_1_2','GF_104w_F_2_2','GF_104w_F_3_2','GF_104w_M_1_2','GF_104w_M_2_2')
to_comp = c("52w VS 8w (SPF)","52w VS 8w (GF)", "104w VS 52w (SPF)", "104w VS 52w (GF)", "104w VS 8w (SPF)", "104w VS 8w (GF)")

annot = as.data.frame(colData(dge)[, c("age", "gender")])

norm_counts_wo_out = counts(dge, normalized=T, replaced = T)
norm_counts_wo_out = norm_counts_wo_out[apply(norm_counts_wo_out, 1, sum) != 0,]
mean_counts = apply(norm_counts_wo_out, 1, mean)
sd_counts = apply(norm_counts_wo_out, 1, sd)
z_scores = (norm_counts_wo_out - mean_counts)/sd_counts
# z_score higher than 3.5 or smaller than -3.5
s = cbind(apply(z_scores > 3.5, 2, sum, na.rm = T), apply(z_scores < -3.5, 2, sum, na.rm = T))
s_perc = 100 * s/dim(norm_counts_wo_out)[1]
apply(s,2,sum)
apply(s_perc,2,min)
apply(s_perc,2,max)

tag_col_order = c(grep("GF_8w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("GF_8w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("GF_52w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("GF_52w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("GF_104w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("GF_104w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("SPF_8w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("SPF_8w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("SPF_52w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("SPF_52w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("SPF_104w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("SPF_104w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE))
tag_annot_col = as.data.frame(colData(dge)[, c("gender", "age","type")])
tag_annot_col$age = factor(tag_annot_col$age,c("8w", "52w", "104w"))

Differentially expressed genes¶

age_type_data = list(SPF_52w_8w, GF_52w_8w, SPF_104w_52w, GF_104w_52w, SPF_104w_8w, GF_104w_8w)
names(age_type_data) = to_comp
age_type_deg = extract_diff_expr_genes(age_type_data, "age-effect/age_type/")

Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in stack.default(getgo(rownames(l$sign_fc_deg), "mm10", "geneSymbol")):
“non-vector elements will be ignored”Warning message in stack.default(getgo(rownames(as.data.frame(l$deg)), "mm10", "geneSymbol", :
“non-vector elements will be ignored”

Stats¶

age_type_deg$stat
plot_stat_mat(age_type_deg$stat)

All DEG (Wald padj < 0.05)¶

# Differentially expressed genes
upset(as.data.frame(age_type_deg$deg),nsets = 6)

DEG (Wald padj < 0.05 & abs(FC) > 1.5)¶

upset(as.data.frame(1*(!is.na(age_type_deg$sign_fc_deg))),nsets = 6)

DEG (Wald padj < 0.05 & abs(FC) > 1.5)¶

Log2FC¶

fc_annot = data.frame(comp = c(rep("52w VS 8w",2), rep("104w VS 52w",2), rep("104w VS 8w",2)),
                     type = rep(c("SPF","GF"),3))
rownames(fc_annot) = colnames(age_type_deg$sign_fc_deg)
fc_annot
plot_fc_heatmap(age_type_deg$sign_fc_deg, fc_annot)

Z-score¶

plot_z_score_heatmap(z_scores,
                     rownames(age_type_deg$sign_fc_deg),
                     tag_col_order,
                     tag_annot_col,
                     "All DE genes in comparison between the ages for the types",
                     tag_col_order)

tga_col_order = c(grep("GF_8w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("GF_8w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("GF_52w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("GF_52w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("GF_104w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("GF_104w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("SPF_8w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("SPF_8w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("SPF_52w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("SPF_52w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("SPF_104w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("SPF_104w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE))

comps = list(
    "52w VS 8w (GF)" = c(grep("GF_8w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("GF_8w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("GF_52w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("GF_52w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE)),
    "104w VS 52w (GF)" = c(grep("GF_52w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("GF_52w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("GF_104w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("GF_104w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE)),
    "104w VS 8w (GF)" = c(grep("GF_8w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("GF_8w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("GF_104w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("GF_104w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE)),
    "52w VS 8w (SPF)" = c(grep("SPF_8w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("SP_8w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("SPF_52w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("SP_52w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE)),
    "104w VS 52w (SPF)" = c(grep("SPF_52w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("SP_52w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("SPF_104w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("SP_104w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE)),
    "104w VS 8w (SPF)" = c(grep("SPF_8w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("SP_8w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("SPF_104w_F_+", colnames(norm_counts), perl=TRUE, value=TRUE),
                  grep("SP_104w_M_+", colnames(norm_counts), perl=TRUE, value=TRUE))    
)

for(comp in names(comps)){
    plot_z_score_heatmap(z_scores,
                     rownames(age_type_deg$sign_fc_deg)[!is.na(age_type_deg$sign_fc_deg[,comp])],
                     tag_col_order,
                     tag_annot_col,
                     paste("DE genes in", comp),
                     comps[[comp]])
}

Co-expression (WGCNA)¶

DEG into gene co-expression network¶

White: up-regulated
Black: down-regulated

	52w VS 8w	104w VS 52w	104w VS 8w
SPF
GF

#par(mfrow=c(2,3),mar=c(0,0,0,0))
#col_52w_vs_8w_SPF = get_deg_colors(age_type_deg, "52w VS 8w (SPF)", connected_gene_colors, module_nb)
#plot_net_with_layout(net, col_52w_vs_8w_SPF, pal2, layout, add_legend = FALSE)
#col_52w_vs_8w_GF = get_deg_colors(age_type_deg, "104w VS 52w (SPF)", connected_gene_colors, module_nb)
#plot_net_with_layout(net, col_52w_vs_8w_GF, pal2, layout, add_legend = FALSE)
#col_104w_vs_52w_SPF = get_deg_colors(age_type_deg, "104w VS 8w (SPF)", connected_gene_colors, module_nb)
#plot_net_with_layout(net, col_104w_vs_52w_SPF, pal2, layout, add_legend = FALSE)
#col_104w_vs_52w_GF = get_deg_colors(age_type_deg, "52w VS 8w (GF)", connected_gene_colors, module_nb)
#plot_net_with_layout(net, col_104w_vs_52w_GF, pal2, layout, add_legend = FALSE)
#col_104w_vs_8w_SPF = get_deg_colors(age_type_deg, "104w VS 52w (SPF)", connected_gene_colors, module_nb)
#plot_net_with_layout(net, col_104w_vs_8w_SPF, pal2, layout, add_legend = FALSE)
#col_104w_vs_8w_GF = get_deg_colors(age_type_deg, "104w VS 8w (SPF)", connected_gene_colors, module_nb)
#plot_net_with_layout(net, col_104w_vs_8w_GF, pal2, layout, add_legend = FALSE)

Z-score in modules¶

mod_pal = pal2
names(mod_pal) = paste("ME", names(pal2), sep='')
names(mod_pal) = replace(names(mod_pal), which(names(mod_pal) == 'ME0'), "No module")
annot_colors = list(
    module = mod_pal
)

plot_z_score_heatmap_with_modules(z_scores,
                                  rownames(z_scores),
                                  tag_col_order,
                                  tag_annot_col,
                                  "All genes")
comps = c("52w VS 8w (GF)","104w VS 52w (GF)","104w VS 8w (GF)",
          "52w VS 8w (SPF)", "104w VS 52w (SPF)", "104w VS 8w (SPF)")
for(comp in comps){
    plot_z_score_heatmap_with_modules(z_scores,
                                     rownames(age_type_deg$sign_fc_deg)[!is.na(age_type_deg$sign_fc_deg[,comp])],
                                     tag_col_order,
                                     tag_annot_col,
                                     paste("DE genes in", comp))
}

for(comp in comps){
    plot_top_deg_in_modules(age_type_deg$sign_fc_deg, comp, connected_gene_colors)
}
options(repr.plot.width=7, repr.plot.height=7)

GO analysis¶

full_go_desc = age_type_deg$GO_wall[[1]][,"term"]
names(full_go_desc) = age_type_deg$GO_wall[[1]][,"category"]
comp = colnames(age_type_deg$over_represented_GO)
comp = comp[4:length(comp)]

Biological process¶

Dot-plot with the most over-represented BP GO (20 most significant p-values for the different comparison)

plot_top_go(age_type_deg,
            "BP",
            40)

Using term, id as id variables
Using term, id as id variables

Network based on description similarity

BP_network = create_GO_network(age_type_deg, "BP", BP_GO)

	52w VS 8w	104w VS 52w	104w VS 8w
SPF
GF

par(mfrow=c(2,3),mar=c(0,0,0,0))
plot_GO_networks(BP_network, "52w VS 8w (SPF)", full_go_desc, plot_interactive = FALSE)
plot_GO_networks(BP_network, "104w VS 52w (SPF)", full_go_desc, plot_interactive = FALSE)
plot_GO_networks(BP_network, "104w VS 8w (SPF)", full_go_desc, plot_interactive = FALSE)
plot_GO_networks(BP_network, "52w VS 8w (GF)", full_go_desc, plot_interactive = FALSE)
plot_GO_networks(BP_network, "104w VS 52w (GF)", full_go_desc, plot_interactive = FALSE)
plot_GO_networks(BP_network, "104w VS 8w (GF)", full_go_desc, plot_interactive = FALSE)

52w VS 8w (SPF)¶

col = get_GO_network_col(BP_network, "52w VS 8w (SPF)")
dotRes = getAmigoTree(goIDs=names(col),
                      color=col,
                      filename="../results/dge/age-effect/age_type/go/52w_VS_8w_SPF",
                      picType="png",
                      saveResult=TRUE)

GO Tree at "../results/dge/age-effect/age_type/go/52w_VS_8w_SPF.png"

52w VS 8w (GF)¶

col = get_GO_network_col(BP_network, "52w VS 8w (GF)")
dotRes = getAmigoTree(goIDs=names(col),
                      color=col,
                      filename="../results/dge/age-effect/age_type/go/52w_VS_8w_GF",
                      picType="png",
                      saveResult=TRUE)

GO Tree at "../results/dge/age-effect/age_type/go/52w_VS_8w_GF.png"

104w VS 52w (SPF)¶

col = get_GO_network_col(BP_network, "104w VS 52w (SPF)")
dotRes = getAmigoTree(goIDs=names(col),
                      color=col,
                      filename="../results/dge/age-effect/age_type/go/104w_VS_52w_SPF",
                      picType="png",
                      saveResult=TRUE)

GO Tree at "../results/dge/age-effect/age_type/go/104w_VS_52w_SPF.png"

104w VS 52w (GF)¶

col = get_GO_network_col(BP_network, "104w VS 52w (GF)")
dotRes = getAmigoTree(goIDs=names(col),
                      color=col,
                      filename="../results/dge/age-effect/age_type/go/104w_VS_52w_GF",
                      picType="png",
                      saveResult=TRUE)

GO Tree at "../results/dge/age-effect/age_type/go/104w_VS_52w_GF.png"

Cellular components¶

Dot-plot with the most over-represented CC GO (20 most significant p-values for the different comparison)

plot_top_go(age_type_deg,
            "CC",
            40)

Using term, id as id variables

Molecular functions¶

Dot-plot with the most over-represented MF GO (20 most significant p-values for the different comparison)

plot_top_go(age_type_deg,
            "MF",
            40)

Using term, id as id variables
Using term, id as id variables

KEGG pathways¶

plot_kegg_pathways(age_type_deg$over_represented_KEGG[,"category"],
                   age_type_deg$fc_deg,
                   "../results/dge/age-effect/age_type/kegg/over_repr_kegg/")

Error in `$<-.data.frame`(`*tmp*`, labels, value = c("", "", "", "", "", : replacement has 77 rows, data has 92
Traceback:

1. plot_kegg_pathways(age_type_deg$over_represented_KEGG[, "category"], 
 .     age_type_deg$fc_deg, "../results/dge/age-effect/age_type/kegg/over_repr_kegg/")
2. suppressMessages(pathview(gene.data = fc_deg, pathway.id = cat, 
 .     species = "Mus musculus", gene.idtype = "Symbol"))
3. withCallingHandlers(expr, message = function(c) invokeRestart("muffleMessage"))
4. pathview(gene.data = fc_deg, pathway.id = cat, species = "Mus musculus", 
 .     gene.idtype = "Symbol")
5. `$<-`(`*tmp*`, labels, value = c("", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", ""))
6. `$<-.data.frame`(`*tmp*`, labels, value = c("", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", ""))
7. stop(sprintf(ngettext(N, "replacement has %d row, data has %d", 
 .     "replacement has %d rows, data has %d"), N, nrows), domain = NA)

plot_kegg_pathways(age_type_deg$under_represented_KEGG[,"category"],
                   age_type_deg$fc_deg,
                   "../results/dge/age-effect/age_type/kegg/under_repr_kegg/")

SPF effect in aging¶

Question: Is there any genes that shows an increasing fold change in SPF with aging while not for GF?

	52w vs 8w for SPF	52w vs 8w for GF	104w vs 52w for SPF	104w vs 52w for GF	Gene number
Set 1	!=		!=		244
Set 2	!=	==	!=	==	92

# set extractions
set1 = rownames(age_type_deg$fc_deg[!is.na(age_type_deg$fc_deg[,"52w VS 8w (SPF)"]) & !is.na(age_type_deg$fc_deg[,"104w VS 52w (SPF)"]),])
set2 = rownames(age_type_deg$fc_deg[!is.na(age_type_deg$fc_deg[,"52w VS 8w (SPF)"]) & is.na(age_type_deg$fc_deg[,"52w VS 8w (GF)"]) & !is.na(age_type_deg$fc_deg[,"104w VS 52w (SPF)"]) & is.na(age_type_deg$fc_deg[,"104w VS 52w (GF)"]),])
# gene numbers
res = matrix(0,ncol = 1, nrow=2, dimnames=list(c("Set 1", "Set 2"),c("Gene number")))
res[1,1] = length(set1)
res[2,1] = length(set2)
res
# log2FC
set1_fc = age_type_deg$fc_deg[set1,]
set1_fc[is.na(set1_fc)] = 0
set2_fc = age_type_deg$fc_deg[set2,]
set2_fc[is.na(set2_fc)] = 0

# plots of the differences
plot(set1_fc[,"52w VS 8w (SPF)"], set1_fc[,"104w VS 52w (SPF)"], xlab = "Log2FC for 52w vs 8w (SPF)", ylab= "Log2FC for 104w vs 52w (SPF)", pch = 20, col = rgb(0,0,1,alpha=0.5))
points(set2_fc[,"52w VS 8w (SPF)"], set2_fc[,"104w VS 52w (SPF)"], pch = 20, col = rgb(1,0,0,alpha=0.5))
legend("topright", pch = 20, col = c(rgb(0,0,1,alpha=0.5),rgb(1,0,0,alpha=0.5)), legend=c("Changing in SPF", "Changing in SPF but not in GF"), cex = 0.7)
abline(h = 0, col = rgb(0,0,0,alpha=0.1))
abline(v = 0, col = rgb(0,0,0,alpha=0.1))

Genes with differential expression with aging in SPF but not in GF (set2)¶

aging_SPF_diff = cbind(set2_fc[,"52w VS 8w (SPF)"], set2_fc[,"104w VS 52w (SPF)"])
colnames(aging_SPF_diff) = c("52w VS 8w (SPF)","104w VS 52w (SPF)")

52w VS 8w (SPF) > 0 and 104w VS 52w (SPF) > 0¶

# 52w VS 8w (SPF) > 0 and 104w VS 52w (SPF) > 0
pos_1_pos_2 = aging_SPF_diff[aging_SPF_diff[,"52w VS 8w (SPF)"]>0 & aging_SPF_diff[,"104w VS 52w (SPF)"]>0,]
investigate_gene_set(pos_1_pos_2)
plot(pos_1_pos_2, col = rgb(1,0,0,alpha=0.5), pch = 19, xlab = "Log2FC for 52w vs 8w (SPF)", ylab= "Log2FC for 104w vs 52w (SPF)")

[1] 28

	Pearson's product-moment correlation

data:  mat[, 1] and mat[, 2]
t = 21.858, df = 26, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9436093 0.9879760
sample estimates:
      cor 
0.9738524

52w VS 8w (SPF) > 0 and 104w VS 52w (SPF) < 0¶

# 52w VS 8w (SPF) > 0 and 104w VS 52w (SPF) < 0
pos_1_neg_2 = aging_SPF_diff[aging_SPF_diff[,"52w VS 8w (SPF)"]>0 & aging_SPF_diff[,"104w VS 52w (SPF)"]<0,]
investigate_gene_set(pos_1_neg_2)
plot(pos_1_neg_2, col = rgb(1,0,0,alpha=0.5), pch = 19, xlab = "Log2FC for 52w vs 8w (SPF)", ylab= "Log2FC for 104w vs 52w (SPF)")

[1] 25

	Pearson's product-moment correlation

data:  mat[, 1] and mat[, 2]
t = -15.545, df = 23, p-value = 1.081e-13
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.9804861 -0.9003858
sample estimates:
       cor 
-0.9555577

52w VS 8w (SPF) < 0 and 104w VS 52w (SPF) > 0¶

# 52w VS 8w (SPF) < 0 and 104w VS 52w (SPF) > 0
neg_1_pos_2 = aging_SPF_diff[aging_SPF_diff[,"52w VS 8w (SPF)"]<0 & aging_SPF_diff[,"104w VS 52w (SPF)"]>0,]
investigate_gene_set(neg_1_pos_2)
plot(neg_1_pos_2, col = rgb(1,0,0,alpha=0.5), pch = 19, xlab = "Log2FC for 52w vs 8w (SPF)", ylab= "Log2FC for 104w vs 52w (SPF)")

[1] 32

	Pearson's product-moment correlation

data:  mat[, 1] and mat[, 2]
t = -11.512, df = 30, p-value = 1.562e-12
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.9519558 -0.8090666
sample estimates:
       cor 
-0.9030072

52w VS 8w (SPF) < 0 and 104w VS 52w (SPF) < 0¶

# 52w VS 8w (SPF) < 0 and 104w VS 52w (SPF) < 0
neg_1_neg_2 = aging_SPF_diff[aging_SPF_diff[,"52w VS 8w (SPF)"]<0 & aging_SPF_diff[,"104w VS 52w (SPF)"]<0,]
investigate_gene_set(neg_1_neg_2)
plot(neg_1_neg_2, col = rgb(1,0,0,alpha=0.5), pch = 19, xlab = "Log2FC for 52w vs 8w (SPF)", ylab= "Log2FC for 104w vs 52w (SPF)")

[1] 6

	Pearson's product-moment correlation

data:  mat[, 1] and mat[, 2]
t = 5.4385, df = 4, p-value = 0.005549
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.5328604 0.9934268
sample estimates:
      cor 
0.9385478

Summary¶

#par(mfrow=c(2,2),mar=c(0,0,0,0))
## 52w VS 8w (SPF) < 0 and 104w VS 52w (SPF) > 0
#neg_1_pos_2_col = connected_gene_colors
#sum(names(neg_1_pos_2_col) %in% rownames(neg_1_pos_2))
#neg_1_pos_2_col[which(names(neg_1_pos_2_col) %in% rownames(neg_1_pos_2))] = module_nb + 1
#plot_net_with_layout(net, neg_1_pos_2_col, pal2, layout, add_legend = F)
## 52w VS 8w (SPF) > 0 and 104w VS 52w (SPF) > 0
#pos_1_pos_2_col = connected_gene_colors
#sum(names(pos_1_pos_2_col) %in% rownames(pos_1_pos_2))
#pos_1_pos_2_col[which(names(pos_1_pos_2_col) %in% rownames(pos_1_pos_2))] = module_nb + 1
#plot_net_with_layout(net, pos_1_pos_2_col, pal2, layout, add_legend = F)
## 52w VS 8w (SPF) < 0 and 104w VS 52w (SPF) < 0
#neg_1_neg_2_col = connected_gene_colors
#sum(names(neg_1_neg_2_col) %in% rownames(neg_1_neg_2))
#neg_1_neg_2_col[which(names(neg_1_neg_2_col) %in% rownames(neg_1_neg_2))] = module_nb + 1
#plot_net_with_layout(net, neg_1_neg_2_col, pal2, layout, add_legend = F)
## 52w VS 8w (SPF) > 0 and 104w VS 52w (SPF) < 0
#pos_1_neg_2_col = connected_gene_colors
#sum(names(pos_1_neg_2_col) %in% rownames(pos_1_neg_2))
#pos_1_neg_2_col[which(names(pos_1_neg_2_col) %in% rownames(pos_1_neg_2))] = module_nb + 1
#plot_net_with_layout(net, pos_1_neg_2_col, pal2, layout, add_legend = F)

	All DEG (Wald padj < 0.05)	All over-expressed genes (Wald padj < 0.05 & FC > 0)	All under-expressed genes (Wald padj < 0.05 & FC < 0)	DEG (Wald padj < 0.05 & abs(FC) >= 1.5)	Over-expressed genes (Wald padj < 0.05 & FC >= 1.5)	Under-expressed genes (Wald padj < 0.05 & FC <= -1.5)
52w VS 8w (SPF)	1205	718	487	574	446	128
52w VS 8w (GF)	556	349	207	310	233	77
104w VS 52w (SPF)	1439	860	579	429	272	157
104w VS 52w (GF)	1276	646	630	573	189	384
104w VS 8w (SPF)	2938	1636	1302	1261	847	414
104w VS 8w (GF)	2259	1196	1063	1158	550	608